Research on Chinese Regional Environmental Management Efficiency Based on DEA and Malmquist Index

2011 ◽  
Vol 138-139 ◽  
pp. 1239-1245
Author(s):  
Jian Zhong Wang ◽  
Tian Ming Chen ◽  
Tie Dan Wang ◽  
Wu Yi Zhang
Keyword(s):  
Environmental Management ◽  
Waste Treatment ◽  
Time Series Data ◽  
Pollution Abatement ◽  
Malmquist Index ◽  
Dominant Factor ◽  
Series Data ◽  
Management Efficiency ◽  
Index Study ◽  
Treatment Facilities

Based on the analysis of 30 provinces ( except Tibet) in China , this paper used the environmental staffs' number, completed industrial pollution abatement investment amount, waste treatment facilities’ number, the three wastes’ comprehensive utilization output value, and harmless waste treatment capacity as input and output index. The paper evaluated the 30 provinces’ environmental management efficiency (except Tibet) in 2008 and 2009 by Data Envelopment Analysis, then analyzed the time series data of the 30 provinces from 2003 to 2009 by Malmquist index. Study finds that overall Chinese environmental management efficiency’s TFP index is 0.942 in 2009, and decreasing year by year. Besides, it shows that technology is dominant factor of restricting Chinese environmental management efficiency, So, we should increase the investment in science and technology as to improve the efficiency. In addition, the environmental management scale’ expansion and structure optimization are also necessary methods of improving the efficiency.

scholarly journals Long-Term Economic Sustainability of Humanitarian Logistics—A Multi-Level and Time-Series Data Envelopment Analysis

2021 ◽  
Vol 18 (5) ◽  
pp. 2219
Author(s):  
Matthias Klumpp ◽  
Dominic Loske
Keyword(s):  
Data Envelopment Analysis ◽  
Time Series Data ◽  
Malmquist Index ◽  
Developed Countries ◽  
Efficiency Analysis ◽  
Humanitarian Logistics ◽  
Series Data ◽  
Data Envelopment ◽  
Least Developed Countries ◽  
Window Analysis

Although resources are scarce and outputs incorporate the potential to save human lives, efficiency measurement endeavors with data envelopment analysis (DEA) methods are not yet commonplace in the research and practice of non-government organizations (NGO) and states involved in humanitarian logistics. We present a boot-strapped DEA window analysis and Malmquist index application as a methodological state of the art for a multi-input and multi-output efficiency analysis and discuss specific adaptions to typical core challenges in humanitarian logistics. A characteristic feature of humanitarian operations is the fact that a multitude of organizations are involved on at least two levels, national and supra-national, as well as in two sectors, private NGO and government agencies. This is modeled and implemented in an international empirical analysis: First, a comprehensive dataset from the 34 least developed countries in Africa from 2002 to 2015 is applied for the first time in such a DEA Malmquist index efficiency analysis setting regarding the national state actor level. Second, an analysis of different sections in a Rohingya refugee camp in Bangladesh is analyzed based on a bootstrapped DEA with window analysis application for 2017, 2018, and 2019 quarter data regarding the private NGO level of operations in humanitarian logistics.

scholarly journals EFISIENSI, PRODUKTIVITAS DAN INDEKS KETIDAKSTABILAN PERIKANAN TUNA LONGLINE DAN PANCING TONDA (Efficieny, Productivity and Instability Index of Tuna Longline and Troll Line)

2016 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Budi Wardono
Keyword(s):  
Time Series ◽  
Total Factor Productivity ◽  
Time Series Data ◽  
Malmquist Index ◽  
Series Data ◽  
Productivity Change ◽  
Instability Index ◽  
Factor Productivity ◽  
Important Indicator ◽  
Tuna Longline

<p>ABSTRACT<br /><br />Tuna longline and troll line are two dominant tuna fishing fleets in Palabuhanratu port. Tuna longline and troll line yielded around 7.06 thousand tons or 89.12 % of total fish production. The main problem of tuna industry was thing related to resource and capturing capacity. This study aimed to understand the capacity, efficiency, and total factor productivity of fisheries business of tuna in PPN Palabuhanratu, using Data Envelope Analysis (DEA) approach. The study was done in harbour area of Palabuhanratu, from January to March 2014. The time series data from 2010 to 2013 were obtained, covering the production of tuna longline and marine hook boats, input usage (boat, fuel, feed, fishermen, ice box, trip number, oil, water, capturing device). Under variable return to scale assumption, the result showed that business capacity of tuna in Palabuhanratu has been efficient. According to Malmquist approach, we found an important indicator of business productivity, ie. Index of total factor productivity change. Malmquist index of troll line was 0.851, while the Malmquist index of tuna longline was 1.139. Both indices showed the magnitude of productive change of the fleets. The annual change of total factor productivity could be described by the change of TFPCH from 2010 to 2013, the respective value of each year were 0.480; 1.945 and 1.023. Those showed the magnitude of productive change of fisheries business of tuna in PPN in Palabuhanratu.<br /><br />Keywords: DEA, efficiency, Malmquist index, productivity, troll line, tuna longline, VRS</p><p>-------<br /><br />ABSTRAK<br />Armada perikanan tuna longline dan pancing tonda merupakan armada yang dominan menangkap ikan tuna di Pelabuhan Perikanan Nusantara (PPN) Palabuhanratu.Total produksi dari keduanya sebanyak 7.066,64 ton (89,12%) dari total produksi ikan di Palabuhanratu. Permasalahan utama industri tuna adalah terkait sumber daya dan kapasitas penangkapan tuna. Tujuan penelitian ini untuk mengetahui tingkat efisiensi, perubahan total faktor produktivitas dan indeks ketidakstabilan usaha perikanan tuna dengan menggunakan tuna longline dan pancing tonda di Palabuhanratu dengan pendekatan Data Envelopment Analysis (DEA) dan Indeks Ketidakstabilan (Coppoct Instability Index). Penelitian dilakukan dikawasan PPN Palabuhanratu, Kabupaten Sukabumi, pada bulan Januari – Maret 2014. Data yang digunakan adalah data time series yang dikeluarkan oleh PPN Palabuhanratu dari tahun 2010-2013. Data yang digunakan dalam analisis ini meliputi produksi dari armada tuna long line dan pancing tonda. Adapun input yang digunakan adalah kapal (longline dan pancing tonda), BBM, umpan, nelayan, es, trip, oli, air, alat tangkap. Hasil analisis dengan asumsi variable return to scale (VRS), kapasitas usaha perikanan tuna di Palabuhanratu, pada armada tuna longline dan pancing tonda sudah efisien. Artinya bahwa sumber daya sudah dialokasikan secara efisien, penggunaan input dalam upaya penangkapan tuna sudah efisien. Hasil analisis menggunakan pendekatan indeks Malmquist diperoleh indeks total factor productivity change yang menunjukkan indikator penting produktifitas usaha. Nilai indeks Malmquist untuk amada pancing tonda sebesar 0,851 dan tuna longline sebesar 1,139, menunjukkan besarnya perbandingan perubahan produktivitas antara kedua armada tersebut. Perubahan total faktor produktivitas antar tahun digambarkan dari besarnya perubahan TFPCH dari tahun 2010 sampai dengan 2013 masing-masing besarnya 0,480; 1,945 dan 1,023, yang menunjukan perubahan besarnya produktivitas usaha perikanan tuna di PPN Palabuhanratu tahun 2010 sampai 2013.<br /><br />Kata kunci: DEA, efisiensi, Malmquist index, produktifitas, pancing tonda, tuna longline, VRS</p>

scholarly journals Causing Factors of Urban Sprawl Symptoms in Peri-Urban Areas of Singaraja City, Bali Province

2021 ◽  
Vol 5 (3) ◽  
pp. 134-142
Author(s):  
I Gede Wyana Lokantara ◽  
Farisa Maulinam Amo
Keyword(s):  
Urban Sprawl ◽  
Migrant Workers ◽  
Urban Areas ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Economic Transformation ◽  
Dominant Factor ◽  
Series Data ◽  
City Development ◽  
The City

The development of a city is marked by an increase in population and an increase in the need for space, which causes the city to be unable to accommodate its activities. Hence, physical development of the city to the peri-urban area and urban sprawl occurs. This also happened in Singaraja City, Bali province as proved by the increasing development of settlements in the area. The purpose of this research is to identify the development of Singaraja City spatially by looking at the pattern of city development due to the influence of the urban sprawl and to identify the factors that influence its development. This study used qualitative methods to analyze the spatial transformation through mapping techniques using time series data developed regions and non-woke Singaraja City, so discovered patterns development impact suburban of Singaraja City. It further analyzed the determinant factors causing urban sprawl in suburban Singaraja. The research shows that Singaraja City has experienced urban development towards the suburban of the city by forming a leap-frog development pattern. The suburban of Singaraja City that are most dominantly affected are the western and eastern regions, namely Baktiseraga Village and Banyuning district. The dominant factor that causes urban sprawl in suburban Singaraja City is people's desire to conduct commercial activities amounting to 94.67% with the opening of shops and services to facilitate the needs of students or migrant workers. The emergence of new economic activities initiated by the surrounding population, causing the orientation of economic transformation in the suburbs of Singaraja city to switch to the non-agricultural sector.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Macro-Economic Disequilibrium and Educational Development in Nigeria between 1980-2017

2019 ◽  
Vol 10 (08) ◽  
pp. 20592-21600
Author(s):  
Gbadebo Salako ◽  
Adejumo Musibau Ojo ◽  
Jaji Ayobami Francis
Keyword(s):  
Time Series Data ◽  
Educational Development ◽  
Negative Relationship ◽  
Series Data ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Long Run ◽  
Stationarity Test ◽  
Level I

This study empirically investigates the effects of macroeconomic disequilibrium on educational development in Nigeria. The study employed time series data between 1980 and 2017. Autoregressive Distributed Lag method of estimation was employed. The result revealed that the variables stationarity test were mixed between the first difference I(I) and level I(0). The cointegration result shows that there exist long run relationship between the variables. The result revealed that Balance of payment, Poverty, Debt rate inflation and unemployment exhibited negative relationship with educational development. The estimation result showed that all explanatory variables account for 88% variation of educational development in Nigeria. It is therefore recommended that government should fast track policies that can stabilize inflation and exchange rate in the country. Also, Policies must be formulated to reduce poverty and unemployment.

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